Hybrid decision-making and optimisation framework for manufacturing-remanufacturing closed loop systems
Why this work is in the frame
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Bibliographic record
Abstract
Remanufacturing of end-of-life products is one of the most preferred disposition alternatives to address environmental and economic concerns. In this paper, a hybrid decision-making framework is proposed to address the strategic planning issues of manufacturing-remanufacturing closed loop systems. The framework consists of qualitative and quantitative parts. The AHP-based qualitative part can be used to determine the configuration of the closed-loop network. The framework takes into consideration the product complexity and other characteristics as well as the supply chain network structure. The model considers the concerns of a manufacturer who offers a variety of complex modular products. The second part of the framework provides a quantitative optimisation model of technology implementation. A case study from the washing machine industry sector is used to illustrate the application of the proposed decision-making framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it